Savardi Mattia, Kovács András Bálint, Signoroni Alberto, Benini Sergio
Department of Information Engineering, University of Brescia, via Branze 38, 25123, Brescia, Italy.
Film Department, ELTE University, Budapest, Hungary.
Data Brief. 2021 Apr 20;36:107002. doi: 10.1016/j.dib.2021.107002. eCollection 2021 Jun.
We provide a database containing shot scale annotations (i.e., the apparent distance of the camera from the subject of a filmed scene) for more than 792,000 image frames. Frames belong to 124 full movies from the entire filmographies by 6 important directors: Martin Scorsese, Jean-Luc Godard, Béla Tarr, Federico Fellini, Michelangelo Antonioni, and Ingmar Bergman. Each frame, extracted from videos at 1 frame per second, is annotated on the following scale categories: Extreme Close Up (ECU), Close Up (CU), Medium Close Up (MCU), Medium Shot (MS), Medium Long Shot (MLS), Long Shot (LS), Extreme Long Shot (ELS), Foreground Shot (FS), and Insert Shots (IS). Two independent coders annotated all frames from the 124 movies, whilst a third one checked their coding and made decisions in cases of disagreement. The CineScale database enables AI-driven interpretation of shot scale data and opens to a large set of research activities related to the automatic visual analysis of cinematic material, such as the automatic recognition of the director's style, or the unfolding of the relationship between shot scale and the viewers' emotional experience. To these purposes, we also provide the model and the code for building a Convolutional Neural Network (CNN) architecture for automated shot scale recognition. All this material is provided through the project website, where video frames can also be requested to authors, for research purposes under fair use.
我们提供了一个数据库,其中包含超过792,000个图像帧的景别标注(即拍摄场景中摄像机与拍摄对象之间的表观距离)。这些帧来自6位重要导演的全部作品中的124部完整电影:马丁·斯科塞斯、让-吕克·戈达尔、贝拉·塔尔、费德里科·费里尼、米开朗基罗·安东尼奥尼和英格玛·伯格曼。从视频中每秒提取1帧得到的每个帧,都按照以下景别类别进行了标注:大特写(ECU)、特写(CU)、中近景(MCU)、中景(MS)、中远景(MLS)、远景(LS)、极远景(ELS)、前景镜头(FS)和插入镜头(IS)。两名独立的编码人员对这124部电影中的所有帧进行了标注,同时第三名编码人员检查了他们的编码,并在出现分歧的情况下做出决定。CineScale数据库能够对景别数据进行人工智能驱动的解读,并开启了一系列与电影素材自动视觉分析相关的研究活动,比如导演风格的自动识别,或者景别与观众情感体验之间关系的展开。为了实现这些目标,我们还提供了用于构建卷积神经网络(CNN)架构以进行自动景别识别的模型和代码。所有这些资料都通过项目网站提供,在合理使用的情况下,研究人员也可以向作者索取视频帧。